{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Using TensorFlow backend.\n"
     ]
    }
   ],
   "source": [
    "# load modules \n",
    "import numpy as np\n",
    "import keras\n",
    "from keras.models import Model, load_model\n",
    "from skimage.io import imshow\n",
    "import matplotlib.pyplot as plt\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')\n",
    "np.set_printoptions(suppress=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Load the ANN model and weights"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "model = load_model('model_2.keras')\n",
    "model.load_weights('model_2.weights.best.hdf5')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Load demo dataset  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "DEMO = np.load('DEMO_data.npy')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Showing the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x115ad2be0>"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x115a006d8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "f, axarr = plt.subplots(2,2,figsize=(15,15))\n",
    "axarr[0,0].imshow((DEMO[0].reshape(480,480)))\n",
    "axarr[0,1].imshow((DEMO[1].reshape(480,480)))\n",
    "axarr[1,0].imshow((DEMO[2].reshape(480,480)))\n",
    "axarr[1,1].imshow((DEMO[3].reshape(480,480)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Making prediction "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0.        ],\n",
       "       [0.00001313],\n",
       "       [0.96885455],\n",
       "       [0.98225373]], dtype=float32)"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.predict(DEMO.reshape(-1,480,480,1)/255)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<img src=\"tenor.gif\">"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from IPython.display import HTML\n",
    "HTML('<img src=\"tenor.gif\">')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### "
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
